Integrative Determination of Macromolecular Structures and ...c.ymcdn.com/sites/ · PDF fileIntegrative Determination of Macromolecular Structures and Networks ... Integrative Determination

Integrative Determination of Macromolecular Structures and Networks

Department of Bioengineering and Therapeutic Sciences Department of Pharmaceutical Chemistry

California Institute for Quantitative Biosciences University of California, San Francisco

Andrej Sali http://salilab.org/

UCSF

Integrative Determination of Macromolecular Structures and Networks

Department of Bioengineering and Therapeutic Sciences Department of Pharmaceutical Chemistry

California Institute for Quantitative Biosciences University of California, San Francisco

Andrej Sali http://salilab.org/

UCSF

Composition Stoichiometry Chemical complementarity

X-ray diffraction

Composition Stoichiometry Chemical complementarity

X-ray diffraction

To understand and modulate cellular processes, we need their models.

These models are best generated by considering all available information.

Contents

1. Integrative structure modeling

2. Integrative structure modeling of 26S proteasome

Structural biology: Maximize accuracy, resolution, completeness, and efficiency of the

structural coverage of macromolecular assemblies

Motivation: Models will allow us to understand how machines work, how they evolved, how they can be controlled, modified, and perhaps even designed.

There may be thousands of biologically relevant macromolecular complexes whose structures are yet to be characterized, involved in a few hundred core biological processes.

GroEL chaperonin

flagellar motorHIV virus

nuclear pore complexATP synthase ribosome

tRNA synthetaseRNA polymerase II

02/15/2007

Sali A, Earnest T, Glaeser R, Baumeister W. From words to literature in structural proteomics. Nature 422, 216-225, 2003. Ward A, Sali A, Wilson I. Integrative structural biology. Science 339, 913-915, 2013.

PHYSICS

STATISTICSEXPERIMENT

∫

Integrative Structural Biology for maximizing accuracy, resolution, completeness, and efficiency of structure determination

Use structural information from any source: measurement, first principles, rules; resolution: low or high resolution

to obtain the set of all models that are consistent with it.

INTUITION

Gatheringinformation

Analyzing modelsand information

Samplinggood models

Designing modelrepresentationand evaluation

A description of integrative structure determinationSali et al. Nature 422, 216-225, 2003. Alber et al. Nature 450, 683-694, 2007

Robinson et al. Nature 450, 974-982, 2007 Alber et al. Ann.Rev.Biochem. 77, 11.1–11.35, 2008

Russel et al. PLoS Biology 10, 2012 Ward et al. Science 339, 913-915, 2013

Schneidman et al. Curr.Opin.Str.Biol., 2014.

While it may be hard to live with generalization, it is inconceivable to live without it. Peter Gay, Schnitzler’s Century (2002).

Integrative models from our lab

PCSK9-Fab, Cheng, Agard, Pons

Nup84 complex, Rout, Chait

Nuclear Pore Complex, Rout, Chait

Nuclear Pore Complex transport, Rout,Chait, Aitchison,Chook, Liphardt,Cowburn

26 Proteasome Baumeister

Spindle PoleBody Davis, Muller

Microtubule nucleation Agard

Ribosomes, Frank, Akey

Hsp90 landscape Agard

TRiC/CCC Frydman, Chiu

RyR channel Serysheva, Chiu

Nup84 hub Rout, Chait


Kin28Ccl1

Tfb3

Ssl2Rad3

Tfb1

Tfb5

Tfb2 Ssl1

Tfb4

40S-eIF1-eIF3 Aebersold,Ban

TFIIH Ranish

PhoQ His kinase DeGrado

Substrate folding by Hsp90 Agard

Prion aggregation Prusiner

PDE6 Chu

Actin Chiu

SEA complex Rout, Chait, Dokudovskaya

Nup133, Rout, Chait

Integrative models from our lab

PCSK9-Fab, Cheng, Agard, Pons


Nuclear Pore Complex, Rout, Chait

Nuclear Pore Complex transport, Rout,Chait, Aitchison,Chook, Liphardt,Cowburn

26 Proteasome Baumeister

Spindle PoleBody Davis, Muller

Microtubule nucleation Agard

Ribosomes, Frank, Akey

Hsp90 landscape Agard

TRiC/CCC Frydman, Chiu

RyR channel Serysheva, Chiu

Nup84 hub Rout, Chait


Kin28Ccl1

Tfb3

Ssl2Rad3

Tfb1

Tfb5

Tfb2 Ssl1

Tfb4

40S-eIF1-eIF3 Aebersold,Ban

TFIIH Ranish

PhoQ His kinase DeGrado

Substrate folding by Hsp90 Agard

Prion aggregation Prusiner

PDE6 Chu

Actin Chiu

SEA complex Rout, Chait, Dokudovskaya

Nup133, Rout, Chait

Open source, versions, documentation, wiki, examples, mailing lists, unit testing, bug tracking, ...

Integrative Modeling Platform (IMP) http://integrativemodeling.org

D. Russel, K. Lasker, B. Webb, J. Velazquez-Muriel, E. Tjioe, D. Schneidman, F. Alber, B. Peterson, A. Sali, PLoS Biol, 2012. R. Pellarin, M. Bonomi, B. Raveh, S. Calhoun, C. Greenberg, G.Dong, S.J. Kim, I. Chemmama

IMP C++/Python library

restrainer PMI

Simplicity

Flex

ibili

ty

Domain-specific

Chimera

Model

angle restraint

volume restraint

conjugate gradients

Monte Carlo

harmonic

nonbonded list

particledistance score

IO

connectivity restraint

cross correlation

Domino

rigid bodySAXS

docking

molecule

Representation: Atomic Rigid bodies Coarse-grained Multi-scale Symmetry / periodicity Multi-state systems

Scoring: Density maps EM images Proteomics FRET Chemical and Cys cross-linking Homology-derived restraints SAXS H/D Exchange Native mass spectrometry Genetic interactions Statistical potentials Molecular mechanics forcefields Bayesian scoring Library of functional forms (ambiguity, ...)

Analysis: Clustering Chimera Pymol PDB files Density maps

Sampling: Simplex Conjugate Gradients Monte Carlo Brownian Dynamics Molecular Dynamics Replica Exchange Divide-and-conquer enumeration

http://integrative

Integration across computational resources

Goal: Maximize accuracy, resolution, completeness, and efficiency of the structural coverage of macromolecules Hypothesis

Model

Experiment

We describe the proceedings and conclusions from the first Integrative Methods Task Force

Workshop that was held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in the various experimental fields that are contributing to these integrative studies, experts in integrative modeling, and experts in data archiving addressed a series of central questions. What data should be archived? How should integrative models be represented? How should the data and integrative models be validated? How should the data and models be archived? What information should accompany the publication of integrative models?

Outcome of the First Hybrid / Integrative Methods Task Force Workshop

Andrej Sali, Helen M. Berman, Torsten Schwede, Jill Trewhella, Gerard Kleywegt, Stephen K. Burley, John Markley, Haruki Nakamura, Paul Adams, Alexandre Bonvin, Wah Chiu, Tom Ferrin, Kay Grünewald, Aleksandras Gutmanas, Richard Henderson, Gerhard Hummer, Kenji Iwasaki, Graham Johnson, Cathy Lawson, Frank di Maio, Jens Meiler, Marc Marti-Renom, Guy Montelione, Michael Nilges, Ruth Nussinov, Ardan Patwardhan, Matteo dal Peraro, Juri Rappsilber, Randy Read, Helen Saibil, Gunnar Schröder, Charles Schwieters, Claus Seidel, Dmitri Svergun, Maya Topf, Eldon Ulrich, Sameer Velankar, and John D. Westbrook. Structure, 2015.

Pushing the envelope of structural biology by integration of all available information

• Size

• Static systems in single and multiple states

• Dynamic systems

• Bulk and single molecule views

• Impure samples

• Overlapping with other domains such as systems biology

Contents

1. Integrative structure modeling

2. Integrative structure modeling of 26S proteasome

The 26S proteasome acts at the endof the ubiquitin proteasome pathway

Bohn S. and Förster F. Handbook of Proteolytic Enzymes, 2012

The 26S proteasome architecture

Bohn S. and Förster F. Handbook of Proteolytic Enzymes, 2012

20S Core Particle

19S Regulatory

Particle

19S Regulatory

Particle

How to determine the molecular architecture of the complete 26S proteasome?

The 26S proteasome has been refractive to single methods for many years, presumably because of conformational and compositional heterogeneity:

• dissociation of the 19S particle into heterogeneous subcomplexes during purification and concentration,

• presence of proteasome interacting proteins,

• conformational variability of some 19S subunits.

MappingthePhaseSpaceofModelsforTransportthroughtheNPC

Gathering information and translation into spatial restraints

Overall shape, component positions

Electron microscopy

Component atomic models

X-ray, NMR, homology modeling

Protein-protein contacts

Protein-protein proximities

ProteomicsChemical

cross-linking

RP components and their representation

Rpn3

Rpn5

Rpn6

Rpn7

Rpn9

Rpn12

Rpn1

Rpn2

Rpn8

Rpn11

Rpn10

Rpn13

PC-repeat containing subunits

PCI containing subunits

MPN containing subunits

Ubiquitin receptors

AAA-ATPase hexamer ring

homology model based on PAN structure (Bohn et al, PNAS, 2010).


Atomic

Rpn3

Rpn5

Rpn6

Rpn7

Rpn9

Rpn12

Rpn1

Rpn2

Rpn8

Rpn11

Rpn10

Rpn13




Ubiquitin receptors

Fixed coarse

Flexible coarse

Hybrid



precision, efficiency, availability


Atomic

Rpn3

Rpn5

Rpn6

Rpn7

Rpn9

Rpn12

Rpn1

Rpn2

Rpn8

Rpn11

Rpn10

Rpn13




Ubiquitin receptors

Fixed coarse

Flexible coarse

Hybrid

Restraints: Geometric complementarity Excluded volume

Restraints: Excluded volume

Restraints: Chain connectivity Radius of gyration Excluded volume



precision, efficiency, availability


Particles Symmetry Increment FSC @ 0.5 FSC @ 0.3

375,000 C2 0.5°

8.4 Å 7.1 Å

Cryo-EM map of the S. pombe 26S proteasome

Å

F. Foerster, S. Bohn, W. Baumeister


Particles Symmetry Increment FSC @ 0.5 FSC @ 0.3

375,000 C2 0.5°

8.4 Å 7.1 Å

Cryo-EM map of the S. pombe 26S proteasome

Restraints: Cross-correlation between a model and the map

Å

F. Foerster, S. Bohn, W. Baumeister


Cryo-EM of knockout mutants localizes Rpn10 and Rpn13

Sakata S, Bohn S, Mihalache O, Kiss P, Beck F, Nagy I, Nickell S, Tanaka K, Saeki Y, Förster F, Baumeister W, PNAS, 2012.


Cryo-EM of knockout mutants localizes Rpn10 and Rpn13

Restraints: Positions of Rpn10 and Rpn13 are fixed while sampling other subunits.

Sakata S, Bohn S, Mihalache O, Kiss P, Beck F, Nagy I, Nickell S, Tanaka K, Saeki Y, Förster F, Baumeister W, PNAS, 2012.


Fitting of D. melanogaster Rpn6 X-ray structure into the cryo-EM map localizes Rpn6

Pathare GR, Nagy I, Bohn S, Unverdorben P, Hubert A, Körner R, Nickell S, Lasker K, Sali A, Tamura T, Nishioka T, Förster F, Baumeister W & Bracher A., PNAS, 2012.



Structure - map cross-correlation










Restraints: Position of Rpn6 is fixed while sampling other subunits.





Restraints: Position of Rpn6 is fixed while sampling other subunits.

Similarly, for the AAA-ATPase Rtp1-6 heteromeric ring (Bohn et al, PNAS, 2010).

Cross-linking / mass spectrometry data

Leitner, Walzthoeni, Kahraman, Herzog, Rinner, Beck, Aebersold. MCP, 2010

Disuccinimidyl suberate (DSS)

T. Walzthoni, A. Leitner, M. Beck, R. Aebersold


Inter-molecular cross-linking of exposed Lys residues:

• 12 Rpt-Rpn residue-specific crosslinks (S.p.) • 3 Rpn-Rpn residue-specific crosslinks (S.p.)



T. Walzthoni, A. Leitner, M. Beck, R. Aebersold


Inter-molecular cross-linking of exposed Lys residues:

• 12 Rpt-Rpn residue-specific crosslinks (S.p.) • 3 Rpn-Rpn residue-specific crosslinks (S.p.)



Restraints: upper distance bounds on cross-linked atoms or beads.

Sampling good-scoring 19S structures


Discretization

discretization of the map into 238 anchor points


LocalizationDiscretization


localization of coarse subunit models, subject to proteomics data

enumeration of all configurations with at most 5 violations


Localization FittingDiscretization




local rigid body fitting of alternative atomic subunit models

selection of best subunit models by fitting quality


Localization Fitting RefinementDiscretization






atomic model refinementsubject to cross-linking and position restraints

Elizabeth Villa


Localization Fitting RefinementDiscretization






atomic model refinementsubject to cross-linking and position restraints

Elizabeth Villa

Ensemble of ~0.5 million best-scoring models


a

Number of violated restraints

Num

ber o

f mod

els

3.0 3.5 4.0 4.5 5.0

5000

10000

15000

20000

25000

1 5 10 15 20 25

200000

400000

600000

800000

1000000

1200000

1400000

1600000

0


a


Num

ber o

f mod

els

3.0 3.5 4.0 4.5 5.0

5000

10000

15000

20000

25000

1 5 10 15 20 25

200000

400000

600000

800000

1000000

1200000

1400000

1600000

0

b

ModelCe

ntro

id R

MSD

[Å]

3

4

5

1

0

2

6

c

Correlation acrossall models

Rpn1Rpn2

Rpn3Rpn5Rpn6

Rpn7

Rpn8Rpn9

Rpn10Rpn11

Rpn12

0.96

0.88

0.80

0.72

0.64

0.56

0.48


a


Num

ber o

f mod

els

3.0 3.5 4.0 4.5 5.0

5000

10000

15000

20000

25000

1 5 10 15 20 25

200000

400000

600000

800000

1000000

1200000

1400000

1600000

0

b

ModelCe

ntro

id R

MSD

[Å]

3

4

5

1

0

2

6

d

Cluster 3

Rpn7Cluster 1

Rpn2Rpn1 Rpn11Rpn10 Rpn12Rpn3 Rpn5 Rpn6 Rpn8 Rpn9

Cluster 2

7

PCI6Rpt6

Rpn1

Rpn2Rpn13

Rpn10

Molecular architecture of the 26S proteasome

Julio Ortiz

7

PCI6Rpt6

Rpn1

Rpn2Rpn13

Rpn10

Molecular architecture of the 26S proteasome

Julio Ortiz

Interpretation of the 26S structure evolution, function, modulation

Rpn10Rpn13

Rpn3

Rpn5

Rpn12

Rpn6

Rpn1

Rpn2

PCI containing proteinsUbiquitin receptorsAPC-repeat containing proteins MPN containing proteins

C-130°

Rpn1

Rpn2Rpn10

Rpn1

B

30°

Rpn13

-130°

D

Rpn9

Rpn5

Rpn6

Rpn7

Rpn7

Rpn3

Rpn12

Rpn5

Rpn9

Rpn9

1. Determination of assembly structures and mapping of networks benefit greatly from the inclusion of all available information.

2. Developers and users of open source Integrative Modeling Platform (IMP) are most welcome.

3. Molecular architecture and function of the 26S proteasome.

Summary

Acknowledgements

26S Karen Lasker

Wolfgang Baumeister Friedrich Foerster Stephan Bohn Elizabeth Villa Pia Unverdorben Florian Beck Ganesh Pathare Eri Sakata Stefan Nickell Andreas Bracher Julio Ortiz (movie)

Ruedi Aebersold Thomas Walzthoeni Alexander Leitner Martin Beck

Carol Robinson Florian Stengel

Funding NIH, NSF

Integrative modeling Seung Joong Kim Peter Cimermancic Barak Raveh Ben Webb Charles Greenberg Dina Schneidman Sara Calhoun Daniel Saltzberg Shruthi Viswanath Ilan Chemmama Seth Axen Elina Tjioe Daniel Russel Max Bonomi Riccardo Pellarin Frank Alber Bret Peterson Maya Topf

Mike Rout (RU) Brian Chait (RU) David Agard (UCSF) Tom Ferrin (UCSF) Trisha Davis (Univ of Wash) Mark Winey (U Colorado) Ivan Rayment (U Wisconsin) Wah Chiu (Baylor) Nevan Krogan (UCSF) Robert Stroud (UCSF) Roger Kornberg (Stanford) Stanley Prusiner (UCSF) Jeff Ranish (ISB) Haim Wolfson (TAU) Nenad Ban (ETH) ...

wwPDB Hybrid/Integrative Methods Task ForceHelen Berman Torsten Schwede Jill Trewhella Stephen Burley Gerard Kleywegt

Frank Dimao Klaus Schulten Jens Meiler Gerhard Hummer

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Integrative Determination of Macromolecular Structures and ...c.ymcdn.com/sites/ · PDF fileIntegrative Determination of Macromolecular Structures and Networks ... Integrative Determination